Inteligencia artificial en procesos de reclutamiento de personal. Análisis bibliométrico y tendencias de investigación

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Vieira Salazar, J. A., & Charfuelan Charfuelan, D. V. (2024). Inteligencia artificial en procesos de reclutamiento de personal. Análisis bibliométrico y tendencias de investigación. Revista Ean, (97). https://doi.org/10.21158/01208160.n97.2024.4232

Publicado: Aug 30, 2024

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https://doi.org/10.21158/01208160.n97.2024.4232

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PlumX

Número

Núm. 97 (2024)

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Artículos científicos

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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.

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Jaime Andrés Vieira Salazar

Universidad Nacional de Colombia, Manizales

https://orcid.org/0000-0003-2678-4440

Deysi Viviana Charfuelan Charfuelan

Universidad Nacional de Colombia, Manizales

https://orcid.org/0009-0006-2730-9931

Resumen

En la literatura reciente, la aplicación de la inteligencia artificial (IA) en la gestión de recursos humanos está generando cada vez más interés; no obstante, es evidente la falta de estudios bibliométricos que relacionen la IA con el reclutamiento de personal (RP). Este artículo estudia la producción científica de esta relación utilizando bibliometría y análisis de tendencias de 132 artículos de Scopus y Web of Science, utilizando diferentes herramientas tecnológicas de análisis de información, como RStudio, Tree of Science y Gephi. El análisis temático reveló cuatro clústeres principales de tendencias de investigación sobre la relación entre la IA y el RP: 1) ventajas de la IA en el RP; 2) desafíos éticos que plantea la IA en el RP; 3) IA y toma de decisiones en el RP; 4) la IA como transformadora del proceso de RP. Según la investigación, la IA puede mejorar la eficiencia y la precisión de los procesos de RP, pero también plantea importantes desafíos éticos. Las conclusiones subrayan la necesidad de incluir estudios geográficos y estudios longitudinales para evaluar el impacto a largo plazo de la IA en la ética organizacional y en la eficiencia de los procesos de reclutamiento.

Palabras clave

Inteligencia artificial

Recursos humanos

Toma de decisiones

Administración de personal

Análisis ocupacional - Bibliometría

Referencias (VER)

Anghel, D. (2023). New perspectives for human and artificial intelligence interactions for Leadership e-recruitment. Societies, 13(3), 55. https://doi.org/10.3390/soc13030055

Albert, E. T. (2019). AI in talent acquisition: A review of AI-applications used in recruitment and selection. Strategic HR Review, 18(5), 215-221. https://doi.org/10.1108/SHR-04-2019-0024

Appio, F. P., Cesaroni, F. & Di Minin, A. (2014). Visualizing the structure and bridges of the intellectual property management and strategy literature: A document co-citation analysis. Scientometrics, 101, 623-661. https://doi.org/10.1007/s11192-014-1329-0

Aria, M. & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007

Bhardwaj, G., Singh, S. V. & Kumar, V. (2020). An empirical study of artificial intelligence and its impact on human resource functions. 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM). IEEE Xplore. https://doi.org/10.1109/iccakm46823.2020.9051544

Bhattacharya, P., Obaidat, M. S., Sanghavi, S., Sakariya, V., Tanwar, S. & Hsiao, K. F. (2022). Internet-of-explainable-digital-twins: A case study of versatile corn production ecosystem. 2022 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI). IEEE Xplore. https://doi.org/10.1109/ccci55352.2022.9926502

Black, J. & van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager use it? Business Horizons, 63(2), 215-226. https://doi.org/10.1016/j.bushor.2019.12.001

Black, J. S. & van Esch, P. (2021). AI-enabled recruiting in the war for talent. Business Horizons, 64(4), 513-524. https://doi.org/10.1016/j.bushor.2021.02.015

Brishti, J.K. & Javed, A. (2020). The viability of ai-based recruitment process. Aa systematic literature review.

Bondarouk, T., Parry, E. & Furtmueller, E. (2017). Electronic HRM: Four decades of research on adoption and consequences. The International Journal of Human Resource Management, 28(1), 98-131. https://doi.org/10.1080/09585192.2016.1245672

Chapman, D. S. & Webster, J. (2003). The Use of Technologies in the Recruiting, Screening, and Selection Processes for Job Candidates. International Journal of Selection and Assessment, 11(2-3), 113-120. https://doi.org/10.1111/1468-2389.00234

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. The Mississippi Quarterly, 13(3), 319. https://doi.org/10.2307/249008

Di Stefano, G., Peteraf, M. & Verona, G. (2010). Dynamic capabilities deconstructed: A bibliographic investigation into the origins, development, and future directions of the research domain. Industrial and Corporate Change, 19(4), 1187-1204. https://doi.org/10.1093/icc/dtq027

Drozdowski, P., Rathgeb, C., Dantcheva, A., Damer, N. & Busch, C. (2020). Demographic bias in biometrics: A survey on an emerging challenge. IEEE Transactions on Technology and Society, 1(2), 89-103. https://doi.org/10.1109/tts.2020.2992344

Duan, Y., Edwards, J. & Dwivedi, Y. (2019). Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021

Faliagka, E., Iliadis, L., Karydis, I., Rigou, M., Sioutas, S., Tsakalidis, A. & Tzimas, G. (2013). On-line consistent ranking on e-recruitment: seeking the truth behind a well-formed CV. Artificial Intelligence Review, 42, 515-528. https://doi.org/10.1007/s10462-013-9414-y

Fallucchi, F., Coladangelo, M., Giuliano, R. & William De Luca, E. (2020). Predicting employee attrition using machine learning techniques. Computers, 9(4), 86. https://doi.org/10.3390/computers9040086

Fernández-Martínez, C. & Fernández, A. (2020). AI and recruiting software: Ethical and legal implications. Paladyn: Journal of Behavioral Robotics, 11(1), 199-216. https://doi.org/10.1515/pjbr-2020-0030

Ferrari, R. (2015). Writing narrative style literature reviews. Medical Writing, 24(4), 230-235. https://doi.org/10.1179/2047480615z.000000000329

Fritts, M. & Cabrera, F. (2021). AI recruitment algorithms and the dehumanization problem. Ethics and Information Technology, 23, 791-801. https://doi.org/10.1007/s10676-021-09615-w

Guo, A., Kamar, E., Vaughan, J., Wallach, H. & Morris, M. (2019). Toward fairness in AI for people with disabilities SBG@a research roadmap. ACM SIGACCESS Accessibility and Computing, (125). https://doi.org/10.1145/3386296.3386298

Hemalatha, A., Kumari, P. B., Nawaz, N. & Gajenderan, V. (2021). Impact of artificial intelligence on recruitment and selection of information technology companies. 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE Xplore. https://doi.org/10.1109/icais50930.2021.9396036

Hengstler, M., Enkel, E. & Duelli, S. (2016). Applied artificial intelligence and trust. The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120. https://doi.org/10.1016/j.techfore.2015.12.014

Holm, A. B. (2012). E-recruitment: Towards an ubiquitous recruitment process and candidate relationship management. German Journal of Human Resource Management, 26(3), 241-259. https://doi.org/10.1177/239700221202600303

Huanca-Arohuanca, J. W. (2022). Combate cuerpo a cuerpo para entrar a la Liga de los Dioses: Scopus y Web of Science como fin supremo. Revista Venezolana de Gerencia, 27(edición especial 7), 663-679. https://doi.org/10.52080/rvgluz.27.7.43

Hutchinson, B., Prabhakaran, V., Denton, E., Webster, K., Zhong, Y. & Denuyl, S. (2020). Social biases in NLP models as barriers for persons with disabilities. Cornell University. https://doi.org/10.48550/ARXIV.2005.00813

Johnson, R. D., Stone, D. L. & Lukaszewski, K. M. (2020). The benefits of eHRM and AI for talent acquisition. Journal of Tourism Futures, 7(1), 40-52. https://doi.org/10.1108/JTF-02-2020-0013

Juntunen, M. & Lehenkari, M. (2021). A narrative literature review process for an academic business research thesis. Studies in Higher Education, 46(2), 330-342. https://doi.org/10.1080/03075079.2019.1630813

Köseoglu, M. A. (2020). Identifying the intellectual structure of fields: Introduction of the MAK approach. Scientometrics, 125, 2169-2197. https://doi.org/10.1007/s11192-020-03719-8

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., Clarke, M., Devereaux, P. J., Kleijnen, J. & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. Journal of Clinical Epidemiology, 62(10), e1-e34. https://doi.org/10.1016/j.jclinepi.2009.06.006

Lochner, K. & Preuß, A. (2018). Digitales Recruiting. Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO), 49, 193-202. https://doi.org/10.1007/s11612-018-0425-7

Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60. https://doi.org/10.1016/j.futures.2017.03.006

Marler, J. H. & Parry, E. (2016). Human resource management, strategic involvement and e-HRM technology. The International Journal of Human Resource Management, 27(19), 2233-2253. https://doi.org/10.1080/09585192.2015.1091980

Martín, K. (2022). Ethics of Data and Analytics (1st ed.). Auerbach Publications. https://doi.org/10.1201/9781003278290

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S. & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716679679

Mujtaba, D. F. & Mahapatra, N. R. (2019). Ethical considerations in AI-based recruitment. 2019 IEEE International Symposium on Technology and Society (ISTAS). IEEE Xplore. https://doi.org/10.1109/istas48451.2019.8937920

Nagpal, S., Singh, M., Singh, R. & Vatsa, M. (2019). Deep learning for face recognition: Pride or prejudiced? Cornell University. https://doi.org/10.48550/arXiv.1904.01219

Nawaz, N. & Gomes, A. M. (2020). Artificial intelligence chatbots are new recruiters. (IJACSA) International Journal of Advanced Computer Science and Applications, 10(9). https://doi.org/10.2139/ssrn.3521915

Ochmann, J., Gengler, E., & Laumer, S. (2019). Fairness as a determinant of AI adoption in recruiting: An interview-based study. AIS eLibrary. https://aisel.aisnet.org/digit2019/16

Pedraza-Navarro, I. & Sánchez-Serrano, S. (2022). Análisis de las publicaciones presentes en WoS y Scopus. Posibilidades de búsqueda para evitar literatura fugitiva en las revisiones sistemáticas. Revista Interuniversitaria de Investigación en Tecnología Educativa, (13), 41-61. https://doi.org/10.6018/riite.548361

Pillai, R. & Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal, 27(9), 2599-2629. https://doi.org/10.1108/bij-04-2020-0186

Raghavan, M., Barocas, S., Kleinberg, J. & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the 2020 conference on fairness, accountability, and transparency. Association for Computing Machinery, 469-481. https://doi.org/10.1145/3351095.3372828

Robledo Giraldo, S., Osorio Zuluaga, G. y López-Espinosa, C. (2014). Networking en pequeña empresa: una revisión bibliográfica utilizando la teoría de grafos. Revista Vinculos, 11(2), 6-16. https://revistas.udistrital.edu.co/index.php/vinculos/article/view/9664

Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1, 206-215. https://doi.org/10.1038/s42256-019-0048-x

Serna, I., Morales, A., Fierrez, J., Cebrian, M., Obradovich, N. & Rahwan, I. (2019). Algorithmic discrimination: Formulation and exploration in deep learning-based face biometrics. Cornell University. https://doi.org/10.48550/arXiv.1912.01842

Sohn, K. & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47, 101324. https://doi.org/10.1016/j.tele.2019.101324

Trewin, S. (2018). AI fairness for people with disabilities: Point of view. Cornell University. https://doi.org/10.48550/arXiv.1811.10670

Trewin, S., Basson, S., Muller, M., Branham, S., Treviranus, J., Gruen, D., Hebert, D., Lyckowski, N. & Manser, E. (2019). Considerations for AI fairness for people with disabilities. AI Matters, 5(3), 40-63. https://doi.org/10.1145/3362077.3362086

Upadhyay, A. K. & Khandelwal, K. (2018). Applying artificial intelligence: Implications for recruitment. Strategic HR Review, 17(5), 255-258. https://doi.org/10.1108/SHR-07-2018-0051

Valencia-Hernández, D. S., Robledo, S., Pinilla, R., Duque-Méndez, N. D. & Olivar-Tost, G. (2020). SAP algorithm for citation analysis: An improvement to tree of science. Ingeniería e Investigación, 40(1),45-49. https://doi.org/10.15446/ing.investig.v40n1.77718

Van Esch, P., Black, J. & Ferolie, J. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215-222. https://doi.org/10.1016/j.chb.2018.09.009

Van Esch, P., Stewart Black, J., Franklin, D. & Harder, M. (2020). Al-enabled biometrics in recruiting: Insights from marketers for managers. Australasian Marketing Journal, 29(3), 225-234. https://doi.org/10.1016/j.ausmj.2020.04.003

Vedapradha, R., Hariharan, R. & Shivakami, R. (2019). Artificial intelligence: A technological prototype in recruitment. Journal of Service Science and Management, 12(3), 382-390. https://doi.org/10.4236/jssm.2019.123026

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. https://doi.org/10.1287/isre.11.4.342.11872

Waheed, A., Miao, X., Waheed, S., Ahmad, N. & Majeed, A. (2019). How new HRM practices, organizational innovation, and innovative climate affect the innovation performance in the IT industry: A moderated-mediation analysis. Sustainability, 11(3), 621. https://doi.org/10.3390/su11030621

Trewin, S. (2018). AI fairness for people with disabilities: Point of view. Cornell University. https://doi.org/10.48550/arXiv.1811.10670

Upadhyay, A. K. & Khandelwal, K. (2018). Applying artificial intelligence: Implications for recruitment. Strategic HR Review, 17(5), 255-258. https://doi.org/10.1108/SHR-07-2018-0051